Parallel processing application in traction motor fault diagnosis

Abstract Parallel processing-based system condition monitoring and fault detection are the order of the day for providing greater safety and reliability of the system. Fast and accurate fault diagnosis and detection predictor provides better system protection and maintenance planning of spare parts. In this paper, an attempt has been made to predict the temperature profile of different parts of a traction motor by using parallel processing techniques. The prediction of temperature of different parts of the motor during healthy and faulty condition have been established by developing a thermal model of the machine and transputer based concurrent process predictor has been taken up to assess the thermal behaviour of the machine The significant advantage of this predictor is its capability of providing early warning about the condition of the machine with greater confidence and built in redundancy and thereby ensured the protection of the machine.